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Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

  • D. Lansing TaylorEmail author
  • Albert Gough
  • Mark E. Schurdak
  • Lawrence Vernetti
  • Chakra S. Chennubhotla
  • Daniel Lefever
  • Fen Pei
  • James R. Faeder
  • Timothy R. Lezon
  • Andrew M. Stern
  • Ivet Bahar
Chapter
Part of the Handbook of Experimental Pharmacology book series

Abstract

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Keywords

Computational models of ADME-Tox Computational models of disease DILI Drug development Drug discovery Drug repurposing Induced pluripotent stem cells Microphysiology systems Omics analyses PBPK Personalized medicine Quantitative systems pharmacology Toxicology 

Notes

Acknowledgments

Support from the National Institutes of Health awards UG3DK119973 (DLT), R01DK0017781 (DLT), 1UO1 TR002383 (DLT), U24TR002632 (AG/MS), SBIR HHSN271201800008C UO1CA204826 (CC), DA035778 (IB), and P41 GM103712 (IB) is gratefully acknowledged. We also thank the members of the University of Pittsburgh Drug Discovery Institute, the Department of Computational and Systems Biology, and other collaborators at the University of Pittsburgh and beyond for critical discussions.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • D. Lansing Taylor
    • 1
    • 2
    Email author
  • Albert Gough
    • 1
    • 2
  • Mark E. Schurdak
    • 1
    • 2
  • Lawrence Vernetti
    • 1
    • 2
  • Chakra S. Chennubhotla
    • 1
    • 2
  • Daniel Lefever
    • 1
  • Fen Pei
    • 1
    • 2
  • James R. Faeder
    • 1
    • 2
  • Timothy R. Lezon
    • 1
    • 2
  • Andrew M. Stern
    • 1
    • 2
  • Ivet Bahar
    • 1
    • 2
  1. 1.University of Pittsburgh Drug Discovery InstitutePittsburghUSA
  2. 2.Department of Computational and Systems BiologyUniversity of PittsburghPittsburghUSA

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